Beyond the Hype: A Practical GenAI Resource Guide for Faculty in Technical Disciplines

As faculty that teach technical disciplines, you are in a unique position. You aren’t just figuring out how to use Generative AI; you are teaching the students who will build, deploy, and critically evaluate these tools for years to come.

The challenge is twofold:

  • How can you leverage AI to improve your own teaching (e.g., create coding examples, debug assignments, or design better projects)?

  • How can you effectively integrate AI into your curriculum as a core competency (e.g., teach prompt engineering, model limitations, and AI ethics)?

The internet is flooded with AI resources, and it’s impossible to sift through them all. This post is a practical, curated guide to help you find the most useful resources for your courses without the noise.

Start with IU: Key Local Resources

Before diving into the wider web, start with the excellent resources available directly from IU. These provide the foundational context and policies for our community.

Generative AI 101 Faculty Resources
Description: An overview of the GenAI 101 Course available to all at IU. Also includes a syllabus insert that can be used to promote the course to students.

Kelley School of Business “AI Playbook”
Description: A “living guide” developed by the Kelley School for faculty on the use of generative AI in teaching, grading, and research. It outlines shared values and emphasizes that faculty expertise remains central.

When to use: When you want faculty-facing guidance on when and how to use generative AI in assessments, course design, and feedback workflows.

A Quick Starting Point: Three Actionable Resources

If you want to branch out, here are three high-value resources to review in 10 minutes or less.

  1. For Your Curriculum: Teach CS with AI: Resource Hub for Computer Science Educators

    • What it is: A hub specifically for integrating AI into CS courses. It includes lesson plans, project ideas, and pedagogical strategies for teaching AI in computing.

    • When to use: When you’re not just using AI, but actively teaching AI concepts, ethics, or applications within a CS or Informatics course.

  2. For Your Pedagogy: Harvard University:“Teaching with Gen-AI” resources

    • What it is: High-level guidance from Harvard on course design, with excellent case studies and strategies for handling risks like hallucinations and superficial reasoning.

  3. When to use: Use this before the semester starts. It’s perfect for designing your syllabus, setting AI policies, and building responsible use guidelines into your course from day one.

  4. For Your Students (and You): AI for Education: “Effective Prompting for Educators”

    • What it is: A focused guide on how to write better prompts. It includes frameworks (like the “5 S Framework”) that are perfect for teaching students a structured approach to “prompt engineering.”

    • When to use: When you want to move students beyond simple “ask-and-receive” and teach them how to partner with AI to get better, more reliable, and more complex results.

The Deep Dive: A Curated Resource Library

For those with more time, here is a more comprehensive list organized by task.

1. How to Use AI in Your Classroom (Pedagogy & Assignments)

2. Helping Students (and You) Get Better at Prompting

  • AI for Education: Prompt Library

    • Description: A comprehensive, searchable collection of ready-to-use prompts and templates specifically for educators.

    • When to use: When you need quick, plug-and-play prompt templates for lesson plans, student tasks, or administrative work.

  • More Useful Things — Prompt Repository for Educators

    • Description: A repository of prompts for instructor aids and student exercises, curated by researchers Ethan and Lilach Mollick.

    • When to use: When you want tested, inspiring prompt sets, especially for idea generation or in-class activities.

  • Anthropic Prompt Library 

    • Description: Anthropic’s (maker of Claude) public library of optimized prompts for business, creative, and general tasks.

    • When to use: When you want to show students (or yourself) “what good prompting looks like” from an industry leader.

3. How to Teach AI in Your CS/InF Courses (Curriculum & Literacy)

  • Teach CS with AI: Resource Hub for Computer Science Educators

    • Description: A hub dedicated to integrating AI topics, tools, and teaching strategies in CS courses.

    • When to use: Use when teaching a CS course and you want to integrate AI content (topics, labs, projects) directly.

  • metaLAB at Harvard: The AI Pedagogy Project / AI Guide

    • Description: A curated site with assignments and projects to integrate AI in pedagogical practice, focused on critical thinking.

    • When to use: When you are designing a module on AI literacy, critical AI thinking, or assessing students’ interaction with AI tools.

  • Ideeas Lab: Teaching & AI resources

    • Description: A resource hub with teaching materials and tools, particularly aimed at engineering and technical fields.

    • When to use: When you want resources specifically tailored for engineering domains that integrate AI in assignments.

  • AI for Education: “Generative AI Critical Analysis Activities

    • Description: Classroom activities to help students critically examine AI outputs, ethics, and limitations.

    • When to use: When you want to design modules around AI ethics or have students evaluate AI rather than simply use it.

4. Taking it Further: Building Your Own AI Tools

5. Professional Development & Staying Current

  • IBM Skills Build for Educators: College Educators resources

    • Description: A professional development site offering modules and training materials to build AI fluency and integrate digital skills into teaching.

    • When to use: When you want a structured PD path for yourself or want to build a course around AI literacy and workforce readiness.

  • University of Maine: LearnWithAI initiative

    • Description: A practical, “how-to” oriented site for faculty on integrating AI into courses.

    • When to use: Use when you want a site focused on faculty development and practical course integration.

  • Future-Cymbal Notion Page: Shared collection of AI-Teaching Resources

    • Description: A collaboratively curated Notion page of ideas, links, frameworks on AI in education; less “formal guide,” more open resource aggregation

    • When to use: Use when you want to browse a broad, ever-updating set of ideas rather than a polished handbook.

  • AI Resources – Lance Eaton

    • Description: It collects a wide variety of resources for educators around generative AI in the classroom — such as sample syllabus statements, institutional policy templates, teaching ideas, and faculty development materials.

    • When to use: When you are designing or revising your course syllabus and need clear language about how you will (or won’t) allow AI tools in student work.

  • Newsletters for Staying Current:

    • The Rundown -Daily newsletter summarizing AI news across research, policy, and industry.

    • The Neuron – Broad coverage of emerging AI trends and commentary, often with education-adjacent insights.

    • The Batch – Weekly deep dives into AI research, tools, and development—ideal for those following the tech side.

    • The Algorithmic Bridge | Alberto Romero – Thoughtful essays analyzing AI’s social, ethical, and educational impact.

    • Everyday AI Newsletter – Daily newsletter (and accompanying podcast) aimed at making AI accessible to “everyday people” whether educators, professionals, or non-tech specialists.

Conclusion: Start Small, Start Now

You don’t need to redesign your entire curriculum overnight. The best approach is to start small.

Pick one thing to try this month. It could be using a prompt library to help you write a coding assignment, adapting a syllabus policy, or introducing one critical analysis activity in a senior seminar. By experimenting now, you’ll be better prepared to lead your students in this new, AI-driven landscape.

Did I miss a great resource? Leave a comment and let me know!

Bridging the Gap: What Tech Practitioners Really Want from Computer Science Education

In the spring of 2024, the Computing Research Association (CRA) asked a simple but powerful question: What do industry professionals think about the way we teach computer science today?  as part of a “Practitioner to Professor (P2P)‘ survey that the CRA-Education / CRA-Industry working group is doing.

The response was overwhelming. More than 1,000 experienced computing practitioners—most with over two decades of experience—shared their honest thoughts on how well today’s CS graduates are being prepared for the real world.

These weren’t just any professionals. Over three-quarters work in software development. Many manage technical teams. Most hold degrees in computer science, with Bachelor’s and Master’s being the most common. Half work for large companies, and a majority are employed by organizations at the heart of computing innovation.

So, what did they say?

The Call for More—and Better—Coursework

One of the loudest messages was clear: students need more coursework in core computer science subjects. Respondents recommended about four additional CS courses beyond what’s typical today. Algorithms, computer architecture, and theoretical foundations topped the list.

But it wasn’t just CS classes that practitioners wanted more of. They also suggested expanding foundational courses—especially in math, writing, and systems thinking. It turns out that the ability to write clearly, think statistically, and understand how complex systems interact is as critical as knowing how to code.

It’s Not Just About Programming

When it came to programming languages, the responses painted a nuanced picture. Practitioners agreed: learning to code isn’t the end goal—learning to think like a problem-solver is.

They valued depth over breadth. Knowing one language well was seen as more important than dabbling in many. But they also stressed the importance of being adaptable—able to pick up new languages independently and comfortable working with different paradigms.

Familiarity with object-oriented programming? Definitely a plus. But what mattered most was a student’s ability to approach problems critically, apply logic, and build solutions—regardless of the language.

The Soft Skills Shortfall

One of the most striking critiques was aimed not at technical training, but at the lack of soft skills being taught in undergraduate programs.

Soft skills, they argued, can be taught—but many universities simply aren’t doing it well. Oral communication courses were highlighted as a critical need. And interestingly, several respondents felt that liberal arts programs were doing a better job than engineering-focused ones in nurturing communication, collaboration, and leadership.

Asked to identify the most important communication skills, respondents pointed to the ability to speak confidently in small technical groups, write solid technical documentation, and explain ideas clearly to leaders and clients—both technical and non-technical.

Math Is Still a Must

Despite the rise of high-level frameworks and automation, the industry’s love affair with math is far from over. In fact, 65% of respondents said they enjoyed or pursued more math than their degree required.

Why? Because math is the backbone of emerging fields like AI, machine learning, and data science. It sharpens analytical thinking, cultivates discipline, and builds a foundation for lifelong adaptability.

The most important math subjects? Statistics topped the list, followed by linear algebra, discrete math, calculus, and logic.

Foundations First

The survey didn’t just surface high-level trends—it got specific.

In algorithms, the emphasis was on conceptual thinking, not just implementation. Students should deeply understand how algorithms work, why they matter, and how to analyze them.

In computer architecture, digital logic and memory hierarchy were considered essential. These are the building blocks that enable students to understand modern computing systems, from the ground up.

And when it came to databases? Practitioners wanted a balance: students should learn both the theory (like relational algebra and normalization) and the practice (like SQL and indexing). Real-world readiness depends on both.

Toward a Better Future for CS Education

What makes this survey so impactful is its timing and intent. As technology continues to reshape every industry, there’s a growing urgency to close the gap between academia and the workforce. The P2P Survey is part of a broader movement to do just that.

Endorsed by leading organizations—ABET, ACM, CSAB, and IEEE-CS—this initiative creates a powerful feedback loop between universities and the industry they serve.

So, what’s next? A full report is expected later this year. But the message is already loud and clear: today’s students need a curriculum that not only teaches them how to code, but prepares them to lead, adapt, and thrive in a complex, evolving world.